


import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class        A     (		unittest.TestCase	):







     '''simple docstring'''







     def __init__(				self    :							Any			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							List[str]=7			,				_UpperCamelCase    :							List[str]=3			,				_UpperCamelCase    :							Any=18			,				_UpperCamelCase    :							Union[str, Any]=30			,				_UpperCamelCase    :							List[str]=400			,				_UpperCamelCase    :							List[str]=True			,				_UpperCamelCase    :							List[str]=None			,				_UpperCamelCase    :							List[str]=True			,				_UpperCamelCase    :							List[str]=None			,				_UpperCamelCase    :							List[str]=True			,				_UpperCamelCase    :							str=[0.5, 0.5, 0.5]			,				_UpperCamelCase    :							Optional[int]=[0.5, 0.5, 0.5]			,				_UpperCamelCase    :							Any=False			,				):
      _lowercase:       Dict						=				size if size is not None else {'height': 20, 'width': 20}
      _lowercase:       Optional[int]						=				crop_size if crop_size is not None else {'height': 18, 'width': 18}
      _lowercase:       List[Any]						=				parent
      _lowercase:       Dict						=				batch_size
      _lowercase:       str						=				num_channels
      _lowercase:       str						=				image_size
      _lowercase:       str						=				min_resolution
      _lowercase:       Dict						=				max_resolution
      _lowercase:       List[str]						=				do_resize
      _lowercase:       Optional[Any]						=				size
      _lowercase:       Optional[int]						=				do_center_crop
      _lowercase:       str						=				crop_size
      _lowercase:       Tuple						=				do_normalize
      _lowercase:       str						=				image_mean
      _lowercase:       Union[str, Any]						=				image_std
      _lowercase:       Optional[Any]						=				do_reduce_labels






     def 					UpperCAmelCase__       (				self    :							Dict):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 							__lowerCAmelCase      (			):
 _lowercase:       List[Any]						=				load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"      )

 _lowercase:       Union[str, Any]						=				Image.open(dataset[0]["file"]      )
 _lowercase:       str						=				Image.open(dataset[1]["file"]      )

 return image, map


def 							__lowerCAmelCase      (			):
 _lowercase:       Optional[int]						=				load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"      )

 _lowercase:       Dict						=				Image.open(ds[0]["file"]      )
 _lowercase:       Union[str, Any]						=				Image.open(ds[1]["file"]      )
 _lowercase:       Union[str, Any]						=				Image.open(ds[2]["file"]      )
 _lowercase:       Any						=				Image.open(ds[3]["file"]      )

 return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        A     (		lowerCamelCase__	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						List[str]            =   BeitImageProcessor if is_vision_available() else None

     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       List[str]						=				BeitImageProcessingTester(self)

     @property
     def 					UpperCAmelCase__       (				self    :							str):
      return self.image_processor_tester.prepare_image_processor_dict()

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      _lowercase:       int						=				self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__snake_case			,				"do_resize"))
      self.assertTrue(hasattr(__snake_case			,				"size"))
      self.assertTrue(hasattr(__snake_case			,				"do_center_crop"))
      self.assertTrue(hasattr(__snake_case			,				"center_crop"))
      self.assertTrue(hasattr(__snake_case			,				"do_normalize"))
      self.assertTrue(hasattr(__snake_case			,				"image_mean"))
      self.assertTrue(hasattr(__snake_case			,				"image_std"))

     def 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Union[str, Any]						=				self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size			,				{"height": 20, "width": 20})
      self.assertEqual(image_processor.crop_size			,				{"height": 18, "width": 18})
      self.assertEqual(image_processor.do_reduce_labels			,				__snake_case)

      _lowercase:       int						=				self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=__snake_case)
      self.assertEqual(image_processor.size			,				{"height": 42, "width": 42})
      self.assertEqual(image_processor.crop_size			,				{"height": 84, "width": 84})
      self.assertEqual(image_processor.do_reduce_labels			,				__snake_case)

     def 					UpperCAmelCase__       (				self    :							str):
      pass

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      # Initialize image_processing
      _lowercase:       Dict						=				self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowercase:       str						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				Image.Image)

      # Test not batched input
      _lowercase:       Dict						=				image_processing(image_inputs[0]			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

      # Test batched
      _lowercase:       List[Any]						=				image_processing(__snake_case			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

     def 					UpperCAmelCase__       (				self    :							Any):
      # Initialize image_processing
      _lowercase:       str						=				self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      _lowercase:       List[Any]						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case			,				numpify=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				np.ndarray)

      # Test not batched input
      _lowercase:       Optional[Any]						=				image_processing(image_inputs[0]			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

      # Test batched
      _lowercase:       Tuple						=				image_processing(__snake_case			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

     def 					UpperCAmelCase__       (				self    :							Any):
      # Initialize image_processing
      _lowercase:       Union[str, Any]						=				self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowercase:       List[str]						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case			,				torchify=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				torch.Tensor)

      # Test not batched input
      _lowercase:       Tuple						=				image_processing(image_inputs[0]			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

      # Test batched
      _lowercase:       int						=				image_processing(__snake_case			,				return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)

     def 					UpperCAmelCase__       (				self    :							List[str]):
      # Initialize image_processing
      _lowercase:       Union[str, Any]						=				self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowercase:       Optional[Any]						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case			,				torchify=__snake_case)
      _lowercase:       int						=				[]
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				torch.Tensor)
       maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      _lowercase:       int						=				image_processing(image_inputs[0]			,				maps[0]			,				return_tensors="pt")
      self.assertEqual(
          encoding["pixel_values"].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(
          encoding["labels"].shape			,				(
              1,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(encoding["labels"].dtype			,				torch.long)
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 255)

      # Test batched
      _lowercase:       List[Any]						=				image_processing(__snake_case			,				__snake_case			,				return_tensors="pt")
      self.assertEqual(
          encoding["pixel_values"].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(
          encoding["labels"].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(encoding["labels"].dtype			,				torch.long)
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 255)

      # Test not batched input (PIL images)
      _lowercase:       Optional[Any]						=				prepare_semantic_single_inputs()

      _lowercase:       List[str]						=				image_processing(__snake_case			,				__snake_case			,				return_tensors="pt")
      self.assertEqual(
          encoding["pixel_values"].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(
          encoding["labels"].shape			,				(
              1,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(encoding["labels"].dtype			,				torch.long)
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 255)

      # Test batched input (PIL images)
      _lowercase:       Tuple						=				prepare_semantic_batch_inputs()

      _lowercase:       Optional[Any]						=				image_processing(__snake_case			,				__snake_case			,				return_tensors="pt")
      self.assertEqual(
          encoding["pixel_values"].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(
          encoding["labels"].shape			,				(
              2,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )			,				)
      self.assertEqual(encoding["labels"].dtype			,				torch.long)
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 255)






     def 					UpperCAmelCase__       (				self    :							List[Any]):
      # Initialize image_processing
      _lowercase:       str						=				self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _lowercase:       Dict						=				prepare_semantic_single_inputs()
      _lowercase:       Tuple						=				image_processing(__snake_case			,				__snake_case			,				return_tensors="pt")
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 150)

      _lowercase:       Tuple						=				True
      _lowercase:       Tuple						=				image_processing(__snake_case			,				__snake_case			,				return_tensors="pt")
      self.assertTrue(encoding["labels"].min().item() >= 0)
      self.assertTrue(encoding["labels"].max().item() <= 255)






'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       CodeGenTokenizer
    lowercase__					=       CodeGenTokenizerFast
    lowercase__					=       True
    lowercase__					=       {"""add_prefix_space""": True}
    lowercase__					=       False







    def      lowercase_			( self					:    List[str]      ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a		:   Any	  =	[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
          '<|endoftext|>',
      ]
      a		:   Dict	  =	dict(zip(__snake_case		,     range(len(__snake_case      )      )      )      )
      a		:   str	  =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      a		:   List[str]	  =	{'unk_token': '<unk>'}

      a		:   List[str]	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['vocab_file']      )
      a		:   Dict	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write(json.dumps(__snake_case      ) + '\n'      )
      with open(self.merges_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write('\n'.join(__snake_case      )      )







    def      lowercase_			( self					:    Tuple		,     **__snake_case					:    Dict      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizer.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    List[Any]		,     **__snake_case					:    List[str]      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizerFast.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[Any]      ):
      a		:   Tuple	  =	'lower newer'
      a		:   str	  =	'lower newer'
      return input_text, output_text







    def      lowercase_			( self					:    str      ):
      a		:   List[Any]	  =	CodeGenTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map      )
      a		:   Dict	  =	'lower newer'
      a		:   int	  =	['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
      a		:   List[str]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   Dict	  =	tokens + [tokenizer.unk_token]
      a		:   int	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[Any]      ):
      if not self.test_rust_tokenizer:
        return

      a		:   Optional[int]	  =	self.get_tokenizer()
      a		:   Tuple	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )

      a		:   List[str]	  =	'lower newer'

      # Testing tokenization
      a		:   Optional[int]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Optional[Any]	  =	rust_tokenizer.tokenize(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids without special tokens
      a		:   Tuple	  =	tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case		,     add_prefix_space=__snake_case      )
      a		:   str	  =	rust_tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids with special tokens
      a		:   Optional[int]	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )
      a		:   Optional[int]	  =	tokenizer.encode(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Dict	  =	rust_tokenizer.encode(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing the unknown token
      a		:   Any	  =	tokens + [rust_tokenizer.unk_token]
      a		:   List[Any]	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    int		,     **__snake_case					:    str      ):
      # It's very difficult to mix/test pretokenization with byte-level
      # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
      pass







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    str=15      ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
          a		:   List[Any]	  =	self.rust_tokenizer_class.from_pretrained(__snake_case		,     **__snake_case      )

          # Simple input
          a		:   Dict	  =	'This is a simple input'
          a		:   int	  =	['This is a simple input 1', 'This is a simple input 2']
          a		:   Dict	  =	('This is a simple input', 'This is a pair')
          a		:   Dict	  =	[
              ('This is a simple input 1', 'This is a simple input 2'),
              ('This is a simple pair 1', 'This is a simple pair 2'),
          ]

          # Simple input tests
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )







    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Any	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     pad_token='<pad>'      )

      # Simple input
      a		:   Union[str, Any]	  =	'This is a simple input'
      a		:   Dict	  =	['This is a simple input looooooooong', 'This is a simple input']
      a		:   int	  =	('This is a simple input', 'This is a pair')
      a		:   List[Any]	  =	[
          ('This is a simple input loooooong', 'This is a simple input'),
          ('This is a simple pair loooooong', 'This is a simple pair'),
      ]

      a		:   Optional[int]	  =	tokenizer.pad_token_id

      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding='max_length'		,     max_length=30		,     return_tensors='np'      )
      a		:   Union[str, Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )
      a		:   Optional[Any]	  =	tokenizer(*__snake_case		,     padding='max_length'		,     max_length=60		,     return_tensors='np'      )
      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )

      # s
      # test single string max_length padding
      self.assertEqual(out_s['input_ids'].shape[-1]		,     30      )
      self.assertTrue(pad_token_id in out_s['input_ids']      )
      self.assertTrue(0 in out_s['attention_mask']      )

      # s2
      # test automatic padding
      self.assertEqual(out_sa['input_ids'].shape[-1]		,     33      )
      # long slice doesn't have padding
      self.assertFalse(pad_token_id in out_sa['input_ids'][0]      )
      self.assertFalse(0 in out_sa['attention_mask'][0]      )
      # short slice does have padding
      self.assertTrue(pad_token_id in out_sa['input_ids'][1]      )
      self.assertTrue(0 in out_sa['attention_mask'][1]      )

      # p
      # test single pair max_length padding
      self.assertEqual(out_p['input_ids'].shape[-1]		,     60      )
      self.assertTrue(pad_token_id in out_p['input_ids']      )
      self.assertTrue(0 in out_p['attention_mask']      )

      # p2
      # test automatic padding pair
      self.assertEqual(out_pa['input_ids'].shape[-1]		,     52      )
      # long slice pair doesn't have padding
      self.assertFalse(pad_token_id in out_pa['input_ids'][0]      )
      self.assertFalse(0 in out_pa['attention_mask'][0]      )
      # short slice pair does have padding
      self.assertTrue(pad_token_id in out_pa['input_ids'][1]      )
      self.assertTrue(0 in out_pa['attention_mask'][1]      )







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   int	  =	'$$$'
      a		:   Dict	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     bos_token=__snake_case		,     add_bos_token=__snake_case      )

      a		:   int	  =	'This is a simple input'
      a		:   Tuple	  =	['This is a simple input 1', 'This is a simple input 2']

      a		:   Any	  =	tokenizer.bos_token_id

      a		:   Union[str, Any]	  =	tokenizer(__snake_case      )
      a		:   Dict	  =	tokenizer(__snake_case      )

      self.assertEqual(out_s.input_ids[0]		,     __snake_case      )
      self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

      a		:   Optional[Any]	  =	tokenizer.decode(out_s.input_ids      )
      a		:   Optional[Any]	  =	tokenizer.batch_decode(out_sa.input_ids      )

      self.assertEqual(decode_s.split()[0]		,     __snake_case      )
      self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )







    @slow
    def      lowercase_			( self					:    List[Any]      ):
      a		:   Tuple	  =	CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'      )

      a		:   Union[str, Any]	  =	'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
      a		:   Optional[Any]	  =	'\nif len_a > len_b:      result = a\nelse:      result = b'

      a		:   Any	  =	tokenizer.encode(__snake_case      )
      a		:   Union[str, Any]	  =	['^#', re.escape('<|endoftext|>'      ), '^\'\'\'', '^"""', '\n\n\n']
      a		:   List[str]	  =	tokenizer.decode(__snake_case		,     truncate_before_pattern=__snake_case      )
      self.assertEqual(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Any      ):
      pass